Top 5 Machine Learning Interview Questions

The next major leap in computer technology will be related to machine learning and AI. If you're looking for a fun and creative career in programming, artificial intelligence is a great place to start. Check out Udacity's Machine Learning Engineer courses to prepare you for a job in AI programming, and when it's time to find a job, prepare for your machine learning interview questions!

What Is Data Normalization?

Data normalization is the process of organizing existing data attributes within a database before processing the data. The point of normalization is to make it easier and quicker to process data within the set. Ideally, all attributes of a data set will be equally weighted.

What Is the Difference Between Type I and Type II Errors?

Knowing basics of machine learning, like error types, assures your interviewer that you've got a handle on the job. Brush up on straight facts before meeting face to face with a potential employer, and be prepared to explain that Type I errors are false positives, while Type II errors are false negatives. So, Type I errors mean the report shows something is happening, when in fact, nothing is happening. Type II errors are reports that nothing is happening, when in fact, something is.

What Would You Do If Data in a Set is Corrupted or Missing?

There are two basic ways you could deal with this problem. The first is to identify which data is corrupted or empty and then simply delete it. The second method allows you to replace missing or incorrect data with your own data.

How Would You Fix an Imbalanced Data Set?

When working with a data set that is missing important variables, the best solution is probably just to collect more data. If the problem persists, consider changing your collection methods or your sample choices. It's possible that an algorithm change could help the outcome, but you should always ensure a quality data set before moving forward.

What Methods Could You Use to Check the Effectiveness of a Machine Learning Model?

First, you could use cross-validation to segment the data set into sets of training and test sets. Next you might choose the F1 score, the confusion matrix, or the accuracy to check the model's effectiveness. For bonus points, go ahead and pick a favorite.

Machine learning is a complex topic with so many applications. Udacity courses in AI, programming, and deep learning will prepare you for a lucrative and interesting career as a machine learning engineer, data specialist, research scientist, AI engineer, or software engineer in companies like Google, Apple, and Android. With machine learning technologies set to take over all kinds of digital industries, this is one of the safest career paths for a budding programmer.

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